Software Engineering Tech Lead

About usWe're a forward-deployed engineering studio focused on enterprise AI. We embed with clients to help them figure out where AI actually belongs in their business, then build the production systems that get them there. That means real problem-solving and architecture work up front, followed by a paved path the client's own teams can operate and extend.Our clients are large enterprises with billions in revenue, significant growth, and real operational complexity. They include market leaders delivering mission-critical facilities, including the data centers behind hyperscale AI. The work spans users, operations, security, IT, infrastructure, data, and engineering, from discovery and architecture through launch and adoption.You'd work alongside partners who've built at companies such as Meta, Amazon, and Airbnb, and who've founded venture-backed startups. We do not stop at pilots or slideware; we build secure systems that work in real workflows and turn what we learn in the field into reusable technology and delivery patterns.The roleYou'd be the senior technical voice on our engagements: owning architecture, setting the quality bar, and shipping. You'd also work directly with client leaders and practitioners to understand how the business operates, find the problem behind the initial request, define outcomes that matter, and build the relationships required to move a complex organization forward.This is a small team. We need someone who can take an ambiguous problem and run with it, not someone who needs a backlog handed to them. You should be comfortable moving from a workflow or executive conversation into architecture, code, debugging, delivery planning, and a clear explanation of the trade-offs.What you'll ownClient discovery and technical scoping: identify the root problem, define a measurable outcome, and say plainly what is buildable in the time available, and what is not.Architecture and technical direction across client-facing AI applications, agentic workflows, enterprise integrations, APIs, data pipelines, identity, and access.Code quality and production delivery: define what good means, review critical work, unblock hard problems, and stay close enough to the code to verify that systems are reliable beyond the demo stage.Infrastructure for AI workloads, including deployment, environments, CI/CD, observability, reliability, latency, and cost.Security posture and enterprise readiness, including authentication, data handling, operational controls, and navigating security review with client teams.Evaluation and monitoring approaches that connect model behavior to user acceptance, business outcomes, and production readiness.Trusted working relationships across client operations, security, infrastructure, IT, data, engineering, and executive teams; communicate trade-offs clearly and help the organization reach decisions.Mentoring and team development: raise the bar for other engineers and help shape the technical direction, delivery model, and culture of the company.Reusable foundations: turn lessons from individual engagements into reference architectures, components, playbooks, and a paved path for future deployments.What we're looking forRequired8+ years building production software, with a sustained record of strong engineering judgment.Prior tech lead experience. You've owned a system and a team's technical direction, not just tickets.Strong backend and system-design fundamentals across APIs, services, data pipelines, distributed systems, and data platforms.Hands-on infrastructure experience across cloud, CI/CD, deployment, networking, observability, reliability, and cost.Practical security experience, including authentication, authorization, data handling, and enterprise security review.Hands-on experience taking AI-enabled products or workflows into production. You've built with LLMs, agents, or retrieval-augmented generation and have informed opinions about model selection, tool use, evaluations, guardrails, latency, and where AI does, and does not, belong.Strong business judgment and communication range: connect technical choices to operating risk, adoption, economics, and long-term value; explain complex concepts simply; and earn trust with executives and deeply technical teams.Evidence of end-to-end ownership in ambiguous environments where requirements changed, resources were constrained, and the outcome still mattered.Nice to haveExperience in both startup and large-company environments, so you know when to move quickly and when process earns its keep.Enterprise, consulting, solutions architecture, or forward-deployed delivery experience; comfortable in front of a client and on site when an engagement requires it.Experience in engineering, field operations, industrial technology, complex capital programs, or data-center delivery.Data modeling and ontology work.Microsoft 365, Azure, or Copilot experience.Experience as a founder, early startup engineer, first technical leader, or manager of a small, high-performing engineering team.How we workSmall team, direct communication, and short feedback loops. We work in two-week sprints, stay close to product and design, and expect risks and disagreements to surface early. If you think something is a bad idea, we want to hear it before the team commits to it.You'd join as one of the earliest senior technical employees and work directly with the founders and customer leaders. You'll have real say in what we take on, how we scope it, and which technical patterns become part of the firm's operating model. This is a hands-on role with broad influence, not a management position removed from delivery.Why this is interestingEnterprise AI adoption is still largely unsolved. Most of what is in the market consists of pilots that never graduate. You'd be building the systems that do, for clients with the resources, operating complexity, and commitment to put them into production. The hard problems are real ones: data ontology, security, integration, reliability, adoption, and proving that the system is working.The data-center wave adds another dimension. Our clients are helping deliver the capacity behind hyperscale AI, so this role sits at the intersection of enterprise AI adoption and the physical buildout supporting the AI economy. As an early technical leader, you will have substantial responsibility and the chance to shape both the technical platform and the forward-deployed engineering firm built around it.Compensation and growthWe intend to offer a highly competitive package calibrated to the scope of the role. The package is expected to include base salary, performance-based cash compensation, and potentially a meaningful equity opportunity. Final terms will reflect experience, location, responsibilities, and employment structure.The learning curve and influence are substantial: direct exposure to enterprise AI adoption and data-center buildouts, complex customer environments, company-building decisions, and the technical patterns that will shape the practice.